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AT-NCIGT - The National Center for Advanced Technologies for Image Guided Therapy

Marco Palombo, PhD: Relaxed-VERDICT: Decoupling Relaxation and Diffusion for Comprehensive Microstructure Characterisation of Prostate Cancer

Marco Palombo

Marco Palombo, PhD
UKRI Future Leaders Fellow
Centre for Medical Image Computing
Department of Computer Science
University College London


Prostate cancer (PCa) diagnosis requires transrectal biopsy, which is invasive and prone to error. VERDICT [1,2] is a non-invasive imaging technique that exploits properties of endogenous water to infer cancer microstructure through diffusion-weighted MRI (dMRI). Specifically, for prostate, VERDICT is based on a biophysical model of three non-exchanging compartments allowing estimation of intracellular (i.e. epithelium, fic), extracellular-extravascular (i.e. stroma and lumen, fees) and vascular (fvasc) volume fractions, and cell size [2].

Despite the simple description of the biological tissue, VERDICT has shown promising results in clinical settings, discriminating normal from malignant tissue [2] and discriminating Gleason grade 3+3 from 3+4 [3]. However, the original formulation [2] does not account for potential compartmental differences in MR relaxation. Thus, the signal fractions fic, fees, fvasc are effectively relaxation-weighted, which can potentially hamper VERDICT’s diagnostic power.

This talk first introduces the VERDICT framework and reviews previous VERDICT results in PCa. Then, it presents a new VERDICT prostate model, called Relaxed-VERDICT (R-VERDICT), that includes compartment-specific T1/T2 relaxation to provide comprehensive characterization of the relaxation and diffusion properties of the prostate tissue. As a result of the disentanglement of the diffusion and relaxation properties, we show that R-VERDICT provides unbiased imaging markers with enhanced ability to distinguish Gleason grade 3+3, 3+4 and >=4+3.

[1] Panagiotaki E, et al. (2014) Noninvasive Quantification of Solid Tumor Microstructure Using VERDICT MRI. Cancer Res 74(7):1902-1912;

[2] Panagiotaki E, et al. (2015) Microstructural Characterization of Normal and Malignant Human Prostate Tissue with Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors Magnetic Resonance Imaging. Invest Radiol 50(4):218-227;

[3] Johnston EW, et al. (2019) VERDICT MRI for Prostate Cancer: Intracellular Volume Fraction versus Apparent Diffusion Coefficient. Radiology 291(2):391-397.

Short Bio

Marco holds a BSc and MSc in Physics from the Sapienza University of Rome (Italy).  In 2014, Marco completed his PhD in Biophysics at the Sapienza University focusing on biophysical modeling of brain microstructure and magnetic resonance (MR) techniques based on water diffusion. Marco’s first postdoctoral position investigated MR spectroscopy techniques based on metabolite diffusion, computational modelling and image processing at the Molecular Imaging Research Center (MIRCen) in Paris (France); supervised by Dr. Julien Valette. Since 2016 Marco joined the Centre for Medical Image Computing (CMIC) at the Department of Computer Science at UCL (London, UK) as senior Research Associate, closely working with Prof. Daniel Alexander and Prof. Hui Zhang. Marco has been recently awarded the prestigious UKRI Future Leaders Fellowship with the aim of promoting MRI as a diagnostic and research tool by enabling microstructural specificity through biophysical/computational modeling and multi-dimensional/modal MR acquisitions.

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